Segmentation is the process of extracting anatomic configurations from images. Many applications in medicine require segmentation of standard anatomy in volumetric images acquired from CT, MRI and other imaging devices. Clinicians, or other professionals, often use segmentation for treatment planning. For example, segmentation may be used for radiation therapy planning such that radiation treatment may be delivered, at a desired dosage, to a target tissue. Currently, segmentation can be performed manually, in which the clinician examines individual image slices and manually draws two-dimensional contours of a relevant organ in each slice. The hand-drawn contours are then combined to produce a three-dimensional representation of the relevant organ. Alternatively, the clinician may use an automatic algorithm for segmentation.
Most structures, however, are still delineated manually slice-by-slice in volumetric medical datasets. Segmentation by hand is tedious and time-consuming, requiring significant expert knowledge to execute. For example, for some applications like radiotherapy in the head and neck region, the segmentation step is one of the main limitations for patient throughput in the clinical workflow. Generally, the clinician must select a slice of the image in which the structure is clearly visible and window-level settings may be manually adjusted to such that a particular region of the image is more clearly visible. Subsequently the contouring process is continued in adjacent slices. As the image contrast often changes from slice to slice, visualization settings such as the window-level setting must be adjusted accordingly for each slice. Manually adjusting the window-level settings for each subsequent image slice or for various regions of a single image slice is time-consuming and tedious.